Identification of coherent sections of sentences is a form of text segmentation Processing of output from an automatic speech recognition (ASR) system is a widely applicable scenario for such text segmentation. In a variety of applicable scenarios, the plain text does not contain any title or annotation to hint about the subtopics discussed. Further, there is a need to segment ASR transcripts to determine a group of sentences wherein such a group need not have to have temporal cohesiveness. Text segmentation has been widely applied in topic identification, text summarization, categorization, information retrieval and dissemination.
Consider a scenario of broadcast news packaging for registered users. The users' profile provides information about the kind of news packages that need to be delivered to the various users. As news is broadcast, it is required to analyze the generated ASR transcripts, identify news segments, and combine multiple segments as a package of audio and video for delivery. Another scenario of interest is scene based segmentation of a video. While it is interesting to determine scenes based on video analysis, it is not completely error-free. In order to complement such an approach, it is useful to analyze the associated audio and convert the same to text form using an ASR system, and the segmentation of the generated text could assist in scene segmentation.